Image Processing and Pattern Recognition Jouko Lampinen

About this presentation In this set of slides we illustrate a bigger problem which uses both morphological operations and other operations that will be introduced soon. In most cases we use morphological operations together with other operations. The most important reason of using them is speed and non-linear processing.

Image analysis of grain material in concrete production Images captured by standard 1200 dpi color scanner Grain shape inputs angularity, flakiness Grain texture inputs Boundary & surface texture FFT based texture features Image Analysis Tool: Matlab standalone application Quality Control Tool: Excel macro package for running and analyzing the Bayesian models (to be discussed)

Example of grains ( mm sieve fraction)

Grain Features Grain Features Measured from the Image Area Major Axis Minor Axis Eccentricity Convex Area Equivalent Diameter Solidity Perimeter Compactness Borderline FFT (5 features related to roughness) Texture 2D FFT (5 features related to surface structure) Morphological Spectrum (roundness) Most of these parameters will be presented in next lectures

Object size and shape characterization Bounding box (rotated along principal axes) Ellipsoid determined by the principal axes Convex hull

Original sand grain image (natural sand)

Thresholded image (natural grains)

Objects filled

Morphological opening (yellow pixels removed)

Labelled objects

Bounding boxes and minor/major axes

Original sand grain image (crushed)

Thresholded image (crushed)

Objects filled

Morphological opening (yellow pixels removed)

Labelled objects

Bounding boxes and minor/major axes

Grain shape analysis: angularity Sharp angles in grains break under compression Measurement: simulate the erosion due to Ice Age by morphological erosion Morphological spectrum: S(r) Amount of material removed by circular structure element of radius r Feature space!!

Example of Morphological Spectra and Angularity Crushed gravel Natural gravel (manufactured by Ice Age) Morphological spectrum We can scientifically compare various gravels